README.md

metadata

library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
  - text-to-sql
  - reinforcement-learning

SLM-SQL: An Exploration of Small Language Models for Text-to-SQL

Important Links

📖Arxiv Paper | 🤗HuggingFace Collection | 🤖ModelScope Collection | 📚GitHub Repository

News

• July 31, 2025: Upload model to modelscope and huggingface.
• July 30, 2025: Publish the paper to arxiv

Introduction

Large language models (LLMs) have demonstrated strong performance in translating natural language questions into SQL queries (Text-to-SQL). In contrast, small language models (SLMs) ranging from 0.5B to 1.5B parameters currently underperform on Text-to-SQL tasks due to their limited logical reasoning capabilities. However, SLMs offer inherent advantages in inference speed and suitability for edge deployment. To explore their potential in Text-to-SQL applications, we leverage recent advancements in post-training techniques. Specifically, we used the open-source SynSQL-2.5M dataset to construct two derived datasets: SynSQL-Think-916K for SQL generation and SynSQL-Merge-Think-310K for SQL merge revision. We then applied supervised fine-tuning and reinforcement learning-based post-training to the SLM, followed by inference using a corrective self-consistency approach. Experimental results validate the effectiveness and generalizability of our method, SLM-SQL. On the BIRD development set, the five evaluated models achieved an average improvement of 31.4 points. Notably, the 0.5B model reached 56.87% execution accuracy (EX), while the 1.5B model achieved 67.08% EX. We will release our dataset, model, and code to github: https://github.com/CycloneBoy/slm_sql.

Framework

Main Results

Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset.

Usage

This model can be loaded and used directly with the Hugging Face transformers library. Below is a basic example for Text-to-SQL generation.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the tokenizer and model
model_name = "cycloneboy/SLM-SQL-0.5B" # You can replace with other models from the table below
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16)

# Example text-to-SQL query
# For Text-to-SQL, you might also need to provide schema information depending on the model's training.
prompt = "Give me the SQL query for customers who placed orders in New York."

# For chat models like Qwen2.5-Coder-0.5B-Instruct, it's often best to use the chat template:
messages = [
    {"role": "user", "content": prompt}
]
chat_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize input
input_ids = tokenizer(chat_input, return_tensors="pt").input_ids.to(model.device)

# Generate SQL query
# Adjust generation parameters as needed. Common ones include max_new_tokens, do_sample, temperature, top_p, num_beams
generated_ids = model.generate(input_ids, max_new_tokens=100, num_beams=1, do_sample=False)

# Decode and print the generated SQL
# Set skip_special_tokens=True to remove special tokens from the output.
generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(generated_text)

Model

Model	Base Model	Train Method	Modelscope	HuggingFace
SLM-SQL-Base-0.5B	Qwen2.5-Coder-0.5B-Instruct	SFT	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-0.5B	Qwen2.5-Coder-0.5B-Instruct	SFT + GRPO	🤖 Modelscope	🤗 HuggingFace
CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct	Qwen2.5-Coder-0.5B-Instruct	SFT + GRPO	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-Base-1.5B	Qwen2.5-Coder-1.5B-Instruct	SFT	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-1.5B	Qwen2.5-Coder-1.5B-Instruct	SFT + GRPO	🤖 Modelscope	🤗 HuggingFace
CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct	Qwen2.5-Coder-1.5B-Instruct	SFT + GRPO	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-Base-0.6B	Qwen3-0.6B	SFT	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-0.6B	Qwen3-0.6B	SFT + GRPO	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-Base-1.3B	deepseek-coder-1.3b-instruct	SFT	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-1.3B	deepseek-coder-1.3b-instruct	SFT + GRPO	🤖 Modelscope	🤗 HuggingFace
SLM-SQL-Base-1B	Llama-3.2-1B-Instruct	SFT	🤖 Modelscope	🤗 HuggingFace

Dataset

Dataset	Modelscope	HuggingFace
SynsQL-Think-916k	🤖 Modelscope	🤗 HuggingFace
SynsQL-Merge-Think-310k	🤖 Modelscope	🤗 HuggingFace
bird train and dev dataset	🤖 Modelscope	🤗 HuggingFace

TODO

• Release inference code
• Upload Model
• Release training code
• Fix bug
• Update doc

Thanks to the following projects

• csc_sql
• open-r1
• OmniSQL

Citation

@misc{sheng2025slmsqlexplorationsmalllanguage,
      title={SLM-SQL: An Exploration of Small Language Models for Text-to-SQL}, 
      author={Lei Sheng and Shuai-Shuai Xu},
      year={2025},
      eprint={2507.22478},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.22478}, 
}

@misc{sheng2025cscsqlcorrectiveselfconsistencytexttosql,
      title={CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning}, 
      author={Lei Sheng and Shuai-Shuai Xu},
      year={2025},
      eprint={2505.13271},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.13271}, 
}